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Autonomous orchestration framework for Claude Code with MemPalace-inspired memory (4-layer stack, 818-token wake-up), parallel-first Agent Teams (6 teammates), Aristotle First Principles methodology, and 4-stage quality gates. 925+ tests, 22 active hooks, automatic learning pipeline.
A coding agent whose claims about its own work resolve to machine-captured evidence: an append-only hash-chained ledger, gates that retry under a numeric ratchet, and a signed bundle carrying its own verifier that anyone can check offline.
Coordinated Agent Team is a prompt-driven multi-agent system for autonomous software delivery. It defines clear agent roles, a deterministic workflow, strict I/O contracts, and artifact-based coordination.
Open-source session-driven development framework for building production software with AI (Claude Code) — scaffolding, quality gates, learning capture. pip install solokit.
Turn Claude Code into an autonomous software development team — 15 speci
8000
alized agents, documentation-first pipeline, TDD slices in parallel waves, and 9 quality gates.
Loop engineering for AI coding agents — turn ad-hoc sessions into a repeatable research → plan → wave-execute → close loop with verification gates. Runs on Claude Code, Codex CLI, Cursor, and Pi. MIT community plugin.
Self-orchestrating multi-agent system for Claude Code — you say WHAT, the AI decides HOW. v7.1: 15 AI agents, Smart Routing, parallel quality gates, skills architecture, plugin & one-command installer. Optimized for Claude Fable 5.
The robustness engine for AI coding agents. A local Rust code graph gives an agent repo-wide understanding, a validation gate that runs before an edit lands, and safe symbol-aware edits. Deep on TypeScript, useful on Rust, local and deterministic.
AI-automated SDLC pipeline with human-controlled quality gates, for Claude Code — spec before code, evidence before pass, a human sign-off at every stage transition. C#/.NET · Node.js · PHP/Laravel.
Ready-to-use project scaffolding with quality gates, testing, and Claude Code integration. Problem This Solves: LLM-assisted development fails by silently expanding scope, degrading quality, and losing architectural intent. This scaffold exists to make those failures impossible without explicit acknowledgement.